Computational Intelligence Evolutionary Computing Evolutionary Clustering Algorithms 1st Edition by Terje Kristensen 1681082998 9781681082998

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ISBN 10: 1681082998
ISBN 13: 9781681082998
Author: Terje Kristensen

This brief text presents a general guideline for writing advanced algorithms for solving engineering and data visualization problems. The book starts with an introduction to the concept of evolutionary algorithms followed by details on clustering and evolutionary programming. Subsequent chapters present information on aspects of computer system design, implementation and data visualization. The book concludes with notes on the possible applications of evolutionary algorithms in the near future This book is intended as a supplementary guide for students and technical apprentices learning machine language, or participating in advanced software programming, design and engineering courses.

Computational Intelligence Evolutionary Computing Evolutionary Clustering Algorithms 1st Table of contents:

Introduction
1.1. OVERVIEW
1.2. GOAL
1.3. OUTLINE
Chapter 1 (Introduction)
Chapter 2 (Background)
Chapter 3 (Evolutionary Algorithms)
Chapter 4 (System Specification)
Chapter 5 (Design and Implementation)
Chapter 6 (Data Visualization)
Chapter 7 (User Interface)
Chapter 8 (Case Study)
Chapter 9 (Discussion)
Chapter 10 (Summary and Future)
Background
2.1. CLUSTERING
2.1.1. Introduction
2.1.2. General Definition
2.1.3. Object Similarity
Proximity Measure for Continuous Values
Proximity Measure for Discrete Values
Proximity Measure for Mixed Values
2.1.4. Clustering Methods
Hierarchical Clustering
Partitional Clustering
Fuzzy Clustering
2.1.5. Cluster Membership
2.1.6. Cluster Validation
Evolutionary Algorithms
3.1. INTRODUCTION
3.1.1. Data Representation Chromosome
3.1.2. Initial Population
3.1.3. Fitness Function
3.1.4. Selection
3.1.5. Reproduction
3.1.6. Stopping conditions
3.2. MATHEMATICAL OPTIMIZATION
3.2.1. Maxima and Mimima
3.2.2. Optimization Problems
3.3. GENETIC ALGORITHMS
3.3.1. Crossover
3.3.2. Mutation
3.3.3. Control Parameters
3.4. GENETIC PROGRAMMING
3.4.1. Tree Based Representation
3.4.2. Fitness Function
3.4.3. Crossover Operators
3.4.4. Mutation Operators
3.5. EVOLUTIONARY PROGRAMMING
3.5.1. Representation
3.5.2. Mutation Operators
3.5.3. Selection Operators
3.6. EVOLUTION STRATEGIES
3.6.1. Generic Evolution Strategies Algorithm
3.6.2. Strategy Parameter
3.6.3. Selection Operator
3.6.4. Crossover Operators
3.6.5. Mutation Operator
3.7. DIFFERENTIAL EVOLUTION
3.7.1. Mutation Operator
3.7.2. Crossover Operator
3.7.3. Selection
3.7.4. Control Parameters
3.8. CULTURAL ALGORITHMS
3.8.1. Belief Space
3.8.2. Acceptance Function
3.8.3. Influence Function
System Specification
4.1. INTRODUCTION
4.2. SYSTEM OBJECTIVE
4.3. FUNCTIONAL REQUIREMENTS
4.3.1. System Input
4.3.2. Cluster Analysis
4.3.3. Visualization
4.4. NON-FUNCTIONAL REQUIREMENTS
4.4.1. Functional Correctness
4.4.2. Extensibility
4.4.3. Maintainability
4.4.4. Portability
4.4.1. Usability
Design and Implementation
5.1. INTRODUCTION
5.2. SYSTEM ARCHITECTURE
5.2.1. Dependency Injection
5.2.2. Open-Closed Principle
5.3. TOOLS AND TECHNOLOGIES
5.3.1. Java
5.3.2. JavaFX
5.3.3. Netbeans
5.3.4. Maven
5.3.5. Git and GitHub
5.3.6. JUnit
5.4. DATA STRUCTURE AND CLUSTERING
5.4.1. Import Data and Data Structure
5.4.2. K-means Algorithm
Complexity of K-Means Operations
5.5. EVOLUTIONARY ALGORITHMS
5.5.1. Genetic Clustering Algorithm
Population Initialization
Fitness Evaluation
Evolve Population
Termination Criteria
Time-Complexity
5.5.2. Differential Evolution Based Clustering Algorithm
Population Initialization
Mutation
Crossover
Termination Criteria
Time-complexity
5.5.3. Selection Operators
Random Selection
Proportional Selection
5.5.4. Mutation Operators
Floating-Point Mutation
5.5.5. Crossover Operators
Variable K Crossover
Adapted Binomial Crossover
5.5.6. Fitness Evaluation
Clustering Metric
Davies-Bouldin Index
Data Visualization
6.1. INTRODUCTION
6.2. DATA TYPES
6.3. VISUAL CHANNELS
6.4. HIGH-DIMENSIONAL DATA
6.4.1. Classical Multi-Dimensional Scaling
6.5. IMPLEMENTATION
Step 1: Normalization
Step 2: Generate Dissimilarity Matrix
Step 3: Dimension Reduction Using CMDS
Step 4: Plotting of Data
User Interface
7.1. INTRODUCTION
7.2. MODEL-VIEW-CONTROLLER
7.3. IMPORT OF DATA
7.4. SELECTING AND INITIALIZING THE ALGORITHMS
7.5. SELECTING AND INITIALIZING EVOLUTIONARY OPERATORS
7.6. VISUALIZATION
A Case Study
8.1. INTRODUCTION
8.2. BENCHMARKING ENVIRONMENT AND CRITERIA
8.2.1. Benchmarking Criteria
8.2.2. Testing the Parameters
8.3. DATA SETS
8.3.1. Hepta Data Set
8.3.2. Iris Data Set
8.3.3. Wine Data Set
8.4. EXPERIMENTS
8.4.1. Hepta
8.4.2. Iris
8.4.3. Wine
CONCLUSION
Discussion
9.1. INTRODUCTION
9.2. DATA REPRESENTATION
9.3. INVALID CLUSTERING STRUCTURES
9.4. ADAPTING EVOLUTIONARY OPERATORS
9.5. HANDLING INVALID INDIVIDUALS
9.5.1. Time-Complexity
9.5.2. Convergence Speed
9.6. FITNESS MEASURE
Summary and Future Directions
10.1. SUMMARY
10.2. ALGORITHM IMPROVEMENTS
10.2.1. Exploitation Abilities
10.2.2. Invalid Clustering Structures
10.3. SYSTEM IMPROVEMENTS
10.3.1. Error Dialog Boxes
10.3.2. System Output
10.4. FUTURE WORK
10.4.1. Parallel Coordinates
10.4.2. Particle Swarm Optimization (PSO)
10.4.3. Clustering Validity Indices

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Tags: Computational, Intelligence, Evolutionary Computing, Clustering Algorithms, Terje Kristensen